How Edge Computing Can Benefit From Stream Processing
This article explores the use cases and challenges of edge computing and how stream processing can help solve these challenges.The integration of edge computing and stream processing increases the efficiency of data processing, simplifies the process of management and control of peripheral devices, and improves the security of devices and data.
Edge computing is a modern architecture that allows companies to quickly analyze large amounts of data by processing the data not far from where it was generated. Edge computing is widely used in medicine, the transportation industry, manufacturing, and other business areas. However, it has some challenges around management, maintenance, and ensuring data security and availability.
Stream processing is a technology that makes it possible to continuously receive and process a large stream of data in real time. It works well with edge computing and can help solve challenges that arise in the operation of edge computing.
This article explores the use cases and challenges of edge computing and how stream processing can help solve these challenges.
What Is Edge Computing?
An edge computing architecture involves processing, analyzing, and storing data at or as close to the data source as possible, rather than sending it to the central server. It enables rapid analysis and processing of data on peripheral devices.
In today's world, there are many devices and sensors that constantly generate huge amounts of data. Transferring this data and processing it in centralized cloud storage is a very long and expensive process. So, companies use edge computing technology to reduce the overall volume of data that needs to be transferred and stored on the central server, reducing data latency and network costs and enabling real-time decision-making.
Edge Computing Use Cases
Edge computing is well-suited for situations where large amounts of data are constantly generated, for scenarios with limited bandwidth, and for processes that require real-time decision-making or low latency.
Autonomous vehicles process a large amount of data that comes from various on-board sensors. They receive information about the speed and direction of movement, location, presence of other vehicles nearby, etc. For their smooth operation, it's critical to process data in real time. This is where edge computing plays a crucial role in allowing large amounts of data to be processed on peripheral computers in milliseconds.
Patient Health Monitoring
Portable devices and medical sensors are becoming more and more popular, which makes it easy to constantly collect important information about patients' health. For example, smartwatches make it possible to measure ECG, blood pressure, and blood oxygen levels.
The analysis of this information in real time makes it possible to detect diseases in their early stages, identify situations when urgent medical assistance is needed, and notify a doctor about them.
Edge computing technology makes it possible to monitor the technical equipment of enterprises in real time. This enables continuous awareness of equipment conditions, early detection and resolution of issues before they cause breakdowns, and accurate prediction of maintenance needs.
For example, in oil and gas production, sensors are installed on drilling equipment, pumps, and pipelines. Peripheral devices process the data from these sensors and monitor the condition of the equipment, detect early signs of wear and leaks, and predict when maintenance is due.
Increasing Agricultural Yield
Peripheral devices allow farmers to collect data about the temperature and humidity of the air, as well as the condition of the soil, in real time. Based on this data, farmers can make decisions about the need for irrigation, fertilization, etc.
Peripheral devices that are equipped with image recognition capabilities can scan crops for signs of disease or pest infestation. This information makes it possible to identify problems in time and take measures to prevent the spread of diseases and control pests.
In smart cities, edge computing can provide automatic monitoring and management of traffic flow, detection and notification of the presence of harmful substances in the air, automatic street lighting, and so on. This allows for the effective development of smart cities to improve public safety and citizen satisfaction.
In e-commerce, personalized customer experience data is used to analyze customer interests and provide special offers. Edge computing allows you to analyze data on customer devices and send only the most important information to the cloud. In addition, it ensures users' privacy as unprotected information is not sent to the cloud.
Challenges of Edge Computing
Edge computing offers many advantages. However, certain challenges need to be addressed when implementing edge computing in order for it to work as efficiently as possible and bring more business benefits.
Peripheral devices generate large amounts of data. However, they often have limited capacity and computing power. Therefore, there are difficulties in data storage, processing, and analysis. Another problem is the possibility of losing valuable information since only partial volumes of data are analyzed at the periphery.
Performance and Latency
If the devices have an unstable network connection, high latency, or bandwidth limitations, that slows down the data transfer to the central server. In addition, it's important to correctly determine what data must be transferred to the server. The limited computing resources of peripherals also cause performance issues.
Security and Privacy
Ensuring data security and privacy is one of the biggest challenges in implementing edge computing. Since many devices are involved in data processing and analysis, the risk of unauthorized access significantly increases. In addition, peripheral devices are usually less physically secure and more vulnerable than cloud servers.
Peripheral devices use a network connection to transfer data to the central cloud. Network failures, increased latency, and packet loss can disrupt the process of data transmission and synchronization. During the operation of peripheral devices, hardware and power failures can occur, which leads to data loss and data unavailability. Therefore, ensuring data availability and accessibility is an important task when implementing edge computing.
Management and Maintenance
Since the number of peripheral devices is very large, the process of their management and maintenance is quite complex. Remote management and monitoring, software updates, and security measures must be implemented.
Using Stream Processing to Resolve Edge Computing Challenges
Stream processing is a technology that allows data to be processed immediately after it is received, which is very important for applications that require real-time data analysis. In addition, data streaming systems provide tools for real-time data aggregation, filtering, enrichment, and analysis. The integration of stream processing and edge computing provides companies with a number of advantages.
Improved Data Analysis
Stream processing provides the ability to aggregate data by combining multiple data points over time, based on specific events, and more. This allows companies to reduce the amount of data and get more meaningful information. In addition, stream processing can filter out irrelevant or noisy data at the boundary, allowing only important information to be transmitted or stored.
The ability to aggregate and filter data reduces the amount of data that needs to be stored, analyzed, and transferred to a centralized server and makes it possible to process and analyze data as it is created.
Reduced Latency and Faster Decision-Making
Streaming systems can run on peripheral devices and enable large volumes of streaming data to be processed. They provide tools to aggregate and filter data based on certain criteria. This reduces the amount of data sent to the cloud, saving bandwidth and cloud processing costs, reducing latency, and increasing performance.
Real-time analytics allows organizations to detect malfunctions, trigger alerts, and obtain important statistics that are essential for quick decision-making. RisingWave Database is a streaming database that allows you to receive streaming data, process it incrementally, and dynamically update the results.
Streaming systems also provide data enrichment possibilities that allow you to add context or additional information to streaming data. This may involve merging data streams with reference data or performing search operations.
Simplified Management and Maintenance of Peripheral Devices
Using stream processing, central control systems can communicate instructions to peripheral devices, manage the distribution of firmware updates, and monitor the update process. It's also possible to receive and analyze data on the serviceability and operating status of peripheral devices and perform real-time monitoring. This allows companies to simplify management and maintenance processes, monitor the performance of devices, and identify potential problems.
RisingWave Cloud is a fully managed streaming platform that makes streaming easy and provides convenient tools for monitoring and managing clusters and users.
Stream processing in edge computing helps detect security threats, such as intrusion attempts or suspicious user behavior, in real time. It also makes it possible to send an automatic response when a potential threat is detected (for example, by blocking malicious network traffic).
Ability to Create a Resilient System
Stream processing allows peripheral devices to analyze and process data as it is generated without having to transmit it to a centralized server. This allows peripherals to make decisions autonomously and reduce dependency on the network.
In case a peripheral device fails, such a system allows other devices to work and make decisions independently.
Streaming systems contribute to a resilient system by providing fault tolerance features such as data replication, load balancing, and automatic failover.
Examples of Integration of Edge Computing and Stream Processing
Edge computing and stream processing are used in a wide range of industries to enable real-time data processing, reduce latency, and facilitate rapid decision-making. Let's look at some examples.
In addition to ATMs, online and mobile banking applications installed on edge devices can collect data about users' behavior and transactions. This data is analyzed using stream processing, which allows financial companies to detect suspicious or fraudulent activities and send instant notifications about them to the bank's security service.
Customer behavior data is used to generate information about customer preferences and needs, allowing financial companies to provide personalized services and recommendations to customers.
Companies such as Nationwide Building Society, Capital One, and PayPal successfully use edge computing and stream processing to improve customer interaction, monitor transactions in real time, and detect fraud.
Peripheral devices equipped with GPS trackers are used to monitor the movement of goods in the supply chain. Using stream processing, retailers can track the location and condition of goods. Retailers can streamline logistics and provide customers with real-time shipment tracking information.
The use of edge computing and stream processing also allows for real-time customer feedback analysis, which can be left by the client through mobile applications. This allows for quick identification and resolution of issues and improves customer satisfaction.
The integration of edge computing and stream processing is used by such well-known companies as Amazon, Walmart, and Albertsons.
Real-time streaming data analysis helps optimize traffic flow and reduce congestion in smart cities. The ability to obtain data about traffic conditions, the location of pedestrians, and other objects makes it possible for autonomous vehicles to make decisions in a fraction of a second.
Examples of using a combination of edge computing and stream processing include autopilot systems by Tesla and Waymo.
The use of peripheral devices makes it easy to remotely monitor the health of patients. The integration of streaming data allows you to quickly obtain important health indicators and respond to emergency situations, such as a sudden increase in blood pressure or heart rate. In addition, the analysis of medical data allows the identification of potential health problems and opportunities for early intervention.
The integration of edge computing with stream processing is used by Philips Healthcare and Siemens Healthineers.
The number of devices continues to grow. Edge computing technology has emerged due to the need to efficiently analyze an ever-increasing amount of data generated by these devices. Edge computing is used in various industries because it has many advantages, including increasing productivity, reducing latency and decision-making time, and reducing costs.
However, a number of difficulties arise in the process of implementing edge computing technology and managing peripheral devices. One of the ways to solve these difficulties is to use stream processing. The integration of edge computing and stream processing increases the efficiency of data processing, simplifies the process of management and control of peripheral devices, and improves the security of devices and data.